Forward deployed AI engineers, embedded in your team | yaab
yaab
Service · Forward Deployed Engineering

Forward deployed AI engineering.

Senior engineers embedded in your team, directing the AI that writes the code and accountable for what ships.

An engineer joins your team, runs discovery, writes the spec, directs and reviews the AI that builds it, and operates the result in production. One person accountable for one problem, from the first conversation to the system running.

the engineer, embedded
What it is

What a forward deployed engineer is.

A forward deployed engineer does not work from a ticket queue. They sit inside your team, with your context, and take a problem from "we need this" to running in production. They talk to your people, decide what to build, direct the AI that builds it, review every gate, and stay accountable for the result.

A product engineer builds one capability for many customers. A forward deployed engineer builds many capabilities for one customer.

The difference

How this differs from staff augmentation.

Staff augmentationForward deployed engineering
What you getHands for your queueOne owner of one problem
Who defines the workYou spec it, they build itThey run discovery and write the spec with you
MethodWhatever the contractor bringsSpecs first, correct defined and measured, review on every gate
Measured byHours loggedShipped behavior against a result defined up front
When it endsThe seat emptiesA documented, tested system your team operates
Who shows up

Who shows up.

Senior before AI

Years shipping production systems, so the review on each gate is informed judgment.

AI-native in practice

They direct code-generating agents daily. Writing code by hand is the exception, and they know when to reject what the agent produced.

Proven on unfamiliar code

They work fluently in a large codebase they did not write, ship changes that pass its tests, and solve problems they have never seen by directing their AI.

Client-facing

They sit in your standups, explain trade-offs to non-technical stakeholders, and disagree with an argument when that is the right call.

Three ways engineers embed

Three ways engineers embed.

Same AI-generated, reviewed delivery in every mode. What changes is how deep the engineer sits in your team.

Forward-deployed engineer

The engineer embeds, runs discovery, owns the spec, directs and reviews the AI, and operates the result.

Embedded engineers

Our full-code-generation engineers join your team and ship inside your sprints. AI-generated, expert-directed and reviewed.

AI Automation Specialists

Specialists who turn the manual and repetitive parts of your SDLC (testing, CI/CD, integrations, data plumbing, releases, internal tools) into AI-driven automation, and ship application code too.

How an embed starts

How an embed starts.

1
Working session

We map the problem and define one observable result. You leave knowing which shape of embed it takes.

2
The engineer is in

They join your channels, your repo and your standups.

3
First increment shipped

The first working piece sets the rhythm and shows the fit early, before anyone commits to a long engagement.

4
Production and handoff

The system runs, documented and tested, and your team can operate it without us.

Engineering in time zones aligned with your working day.

What they build

What they build.

Production features inside your product. Internal tools and automations. AI capabilities in what you already sell: retrieval, copilots, in-product agents. Integrations between systems that do not talk to each other. And the QA automation that protects all of it on every change.

What lands, and what stays yours

What lands, and what stays yours.

The system running in production against the observable result defined in the working session.

The spec it was built against and the tests that prove it, running in your CI.

The code and the repository, including history.

The IP. No license back to us and no runtime dependency on anything of ours.

The engineering brain of what was built: decisions, rules and tests, connected, so whoever continues it starts with context. If you later grow a Company Brain, this is already a module of it.

Done means: the observable result defined in the working session, measured.

If you later grow a Company Brain, what was built here is already a module of it.

Learn about the Brain

Bring the problem.

One working session: we map the problem, define the observable result, and tell you which shape of embed it takes.